FPGA acceleration using high-level languages of a Monte-Carlo method for pricing complex options

Sanchez-Roman, Diego; Moreno, Víctor; López-Buedo, Sergio; Sutter, Gustavo; González, Iván; Gómez-Arribas, Francisco J.; Aracil, Javier

doi:10.1016/j.sysarc.2013.01.004

Cited by 2 publications

References 33 publications

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On the Provision of SaaS-Level Quality of Service within Heterogeneous Private Clouds

Orellana

Caminero

Carrión

2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

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The efficient utilization of computing resources, consisting of multi-core CPUs, GPUs and FPGAs, has become an interesting research problem for achieving high performance on heterogeneous Cloud computing platforms. In particular, FPGA accelerators can provide significant business value in Cloud environments due to its great computing capacity with predictable latency and low power consumption.In this paper, a Software as a Service (SaaS) model is enhanced with Quality of Service (QoS) support, harnessing such heterogeneous hardware architecture (composed of conventional CPUs plus FPGAs as accelerator). More precisely, the proposal takes into account timing user requirements to manage virtual resources. Hence, novel heterogeneous-aware resource allocation and scheduling algorithms are presented, which can be used both on-demand and in-advance. A lineal regression model that predicts the cost of the requested service is combined with a simple heuristic algorithm in order to allocate different types of Virtual Machines (VMs). Moreover, the framework provides the service efficiently by using an adapted scheduling algorithm that combines CPUs and accelerator resources.

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On the Provision of SaaS-Level Quality of Service within Heterogeneous Private Clouds

Orellana

Caminero

Carrión

2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

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The Role of FPGAs in Modern Option Pricing Techniques: A Survey

O Mahony,

Hanzon,

Popovici

2024

Electronics

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In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.

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FPGA acceleration using high-level languages of a Monte-Carlo method for pricing complex options

Cited by 2 publications

References 33 publications

On the Provision of SaaS-Level Quality of Service within Heterogeneous Private Clouds

On the Provision of SaaS-Level Quality of Service within Heterogeneous Private Clouds

The Role of FPGAs in Modern Option Pricing Techniques: A Survey

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